NDLI: Unsupervised Facial Pose Grouping via Garbor Subspace Affinity and Self-Tuning Spectral Clustering

Content Provider	IEEE Xplore Digital Library
Author	Xin Liu Yiu-Ming Cheung
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Hong Kong Baptist Univ., Hong Kong, China (Yiu-Ming Cheung) \|\| Dept. of Comput. Sci. & Technol., Huaqiao Univ., Xiamen, China (Xin Liu)
Abstract	Facial pose grouping plays an important role in the video face recognition. In this paper, we present an unsupervised facial pose grouping approach via Garbor subspace affinity and self-tuning spectral clustering. First, we utilize the local normalization method to reduce the impact of uneven illuminations, and then extract the discriminative appearance features via Gabor wavelet representation. Next, the Garbor subspace affinity method is presented to compute an affinity matrix in terms of the pair wise similarity, in which the facial frames of the same pose always share the smaller pair wise similarities. Finally, we employ the self-tuning spectral clustering algorithm to label the affinity matrix, through which the number of pose groups and the corresponding grouping results can be obtained automatically. Without any label priors, the proposed approach is able to well differentiate the distinct facial poses under uneven illuminations, and the experimental results have shown the satisfactory performances.
Starting Page	2720
Ending Page	2724
File Size	760315
Page Count	5
File Format	PDF
e-ISBN	9781479986972
DOI	10.1109/SMC.2015.475
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lighting Face recognition Face Feature extraction Manifolds Kernel Clustering algorithms self-tuning spectral clustering Facial pose grouping Garbor subspace affinity
Content Type	Text
Resource Type	Article

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